Upshop
Data Scientist
Mississauga
Sponsorship not specified$120k-$140kDetected 13 days ago
PythonCode ReviewGitDatabricksCI/CDGitHub ActionsMachine LearningData EngineeringData ScienceAgentic AIMLOpsForecastingSupply ChainpytestTest AutomationUnityResearchDemand Planning
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odds of building a lasting career here
54Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level III)83
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~45% per draw at Level III). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
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About the role
- In this role, model development goes hand-in-hand with operational reliability: success is measured by accurate forecasts and stable, low-latency, deterministic pipelines running natively on the Databricks Lakehouse.
- You will bridge the gap between applied data science and machine learning engineering.
Responsibilities
- Production Forecasting & Optimization: Develop, calibrate, and tune time-series forecasting engines and downstream supply chain/inventory optimization logic using tree-based ensembles ( LightGBM, CatBoost ) and distributed Python/PySpark.
- Databricks-Native MLOps: Build, schedule, and maintain automated training and batch inference pipelines using Databricks Workflows and Jobs. Leverage MLflow for robust experiment tracking, artifact logging, and model registry management.
- With Data Engineering: Establish strict data contracts, define schema validations, and optimize data ingestion/consumption patterns from upstream Delta tables.
- Implement pre-inference data validation gates (e.g., schema checks, missingness thresholds, null checks) and automated alerting to prevent corrupted data from reaching scoring jobs.
- Tree-Based Ensembles: Hands-on experience developing, tuning, and deploying gradient boosted decision trees-specifically LightGBM and CatBoost -on high-cardinality, tabular, and time-series datasets.
- Databricks Platform: Proven experience building within the Databricks ecosystem, specifically authoring and managing multi-task Databricks Workflows/Jobs, navigating Delta Lake, and using MLflow across the model lifecycle.
- Software Engineering Fundamentals: Solid understanding of clean code principles, modular package design, virtual environments, automated testing ( pytest ), and standard Git workflows (pull requests, branching, code reviews).
- Working in close partnership with Product and Data Engineering, you will own the operational lifecycle of item-store level demand forecasts and downstream replenishment/optimization engines-from distributed feature pipelines to automated Databricks workflows, model tracking, and runtime monitoring.
- Develop, calibrate, and tune time-series forecasting engines and downstream supply chain/inventory optimization logic using tree-based ensembles ( LightGBM, CatBoost ) and distributed Python/PySpark.
- Build, schedule, and maintain automated training and batch inference pipelines using Databricks Workflows and Jobs.
Requirements
- Required Technical Skills
- Data Manipulation & PySpark: Strong proficiency in Python and PySpark for distributed data processing, feature engineering, and memory-conscious transformations across massive retail datasets.
Nice to have
- Databricks Advanced Features: Experience leveraging Delta Live Tables (DLT), Unity Catalog for data and model governance, or Photon compute engine.
- Explainable AI ( XAI ): Experience implementing TreeSHAP or similar interpretability methods within production batch scoring jobs.
- Continuous Integration: Experience setting up or integrating with CI/CD pipelines (e.g., GitHub Actions) to automate testing and deployment into Databricks workspaces.
- Cultural Alignment & Values
- Production Mindset: You believe a model is only finished when it is tested, automated, monitored, and running reliably in production.
- Collaborative Communicator: You easily speak the language of business trade-offs with Product managers and system architecture with Data Engineers.
- The estimated pay ranges for this role are as follows:
- $120,000 - 140,000 CAD
Compensation
- The successful candidate's starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location.
Benefits
- Model & Pipeline Observability: Track pipeline health, monitor runtime performance, and detect feature drift, target drift, and forecast degradation across high-cardinality retail catalogs.
Company info
- We are seeking an engineering-focused Data Scientist to build, operationalize, and maintain production-grade retail forecasting and optimization models.
This listing is sourced directly from Upshop's careers page and normalized into a canonical job model.